What repositories need added to default config.json?

[S0AndS0]

To help @llSourcell fix Git logs, the current config.json file contains entries similar to...

Update notice, 57 repositories are now within the default config.json file the following example configurations are left as is for brevity.

{
  "fixed": "./fixed.json",
  "failed": "./failed.json",
  "defaults": {
    "origin_branch": "master",
    "origin_remote": "origin",
    "source_branch": "master",
    "source_remote": "source",
    "fix_branch": "fix",
    "fix_commit": "Fixes logs",
    "keep_fix_branch": false,
    "no_push": false
  },
  "repos": [
    {
      "dir": "~/git/hub/llSourcell/Bitcoin_Trading_Bot",
      "source": "https://github.com/jaungiers/Multidimensional-LSTM-BitCoin-Time-Series.git"
    },
    {
      "dir": "~/git/hub/llSourcell/How-to-Predict-Stock-Prices-Easily-Demo",
      "source": "git@github.com:jaungiers/LSTM-Neural-Network-for-Time-Series-Prediction.git"
    },
    {
      "dir": "~/git/hub/llSourcell/How_to_simulate_a_self_driving_car",
      "source": "git@github.com:naokishibuya/car-behavioral-cloning.git"
    }
  ]
}

... there are likely more repositories that can be added to automated fixing of Git logs.

If you're willing to help track these repositories down then please either list them as a response to this Issue, or via a Pull Request to the default config.json file.

When adding to this list it would be very kind if you could also notate any extra commands required.

---

Example

• After checkout miloharper/simple-neural-network requires git mv

git mv ./main.py ./demo.py

---

This will help with directing future feature developments of the fix_logs project; currently I'm contemplating adding options such as...

{
  "dir": "~/git/hub/llSourcell/How_to_simulate_a_self_driving_car",
  "source": "git@github.com:naokishibuya/car-behavioral-cloning.git",
  "git_commands": {
    "after_checkout": ["mv", "./main.py", "./demo.py"]
  }
}

... to better facilitate fixing Git logs in an automated fashion.

[S0AndS0]

The config.json file has been updated with repositories that could be easily parsed for source URLs. the following lists those that could be found that require special attention and/or actions to correct.

---

Missing Source Repository

If the source is found then please add it to either the config.json file via Pull Request, or by adding to this Issue

• how_to_deploy_a_keras_model_to_production => https://github.com/moinudeen

• world_models => https://github.com/AppliedDataSciencePartners

• YOLO_Object_Detection => https://github.com/thtrieu

• Intro_to_the_Math_of_intelligence => https://github.com/mattnedrich

• The_evolution_of_gradient_descent => https://github.com/GRYE

• LSTM_Networks => https://github.com/kevin-bruhwiler

• AI_Dresses_Itself => https://github.com/ikostrikov

• Gaussian_Mixture_Models => https://github.com/brianspiering

• Q_Learning_Explained => https://medium.com/@m.alzantot

• Boston_Dynamics_Atlas_Explained => https://github.com/Einsbon/

• linear_regression_live => https://github.com/mattnedrich

• loss_functions_explained => https://github.com/smsaurabhv

• k_means_clustering => https://github.com/gleydson404

• How_to_make_a_text_summarizer => https://github.com/udibr

• navigating_a_virtual_world_with_dynamic_programming => https://github.com/root-ua

• AI_for_Scheduling => https://www.youtube.com/watch?v=nvLJq4GnCg4&feature=youtu.be

• synthetic_gradients_explained => https://iamtrask.github.io/2017/03/21/synthetic-gradients/

• ethereum_demo => https://github.com/eshon

• proximal_policy_optimization => https://learningai.io/projects/2017/07/28/ai-gym-workout.html

• actor_critic => https://towardsdatascience.com/reinforcement-learning-w-keras-openai-actor-critic-models-f084612cfd69

• what_is_an_initial_coin_offering => https://github.com/gustavoguimaraes

• OpenMined_demo => https://github.com/OpenMined

• Stock_Market_Prediction => https://github.com/dineshdaultani

• A_guide_to_coreML => https://github.com/gkswamy98

• Evolutionary_Space_Invaders => https://github.com/citiususc

• Latent_Dirichlet_Allocation => https://github.com/morreene

• naive_bayes_classifier => https://github.com/alanbuzdar

• hyperparameter_optimization_strategies => https://github.com/davidlibland

• random_forests => https://github.com/rushter

• Second_Order_Optimization_Newtons_Method => https://github.com/dsalaj

• tensorflow_image_classifier => https://github.com/xblaster

• Dimensionality_Reduction => https://github.com/rasbt

• logistic_regression_newtons_method => https://github.com/eecs445-f16

• The_Power_of_the_blockchain => https://github.com/k5trismegistus

• How-to-Learn-from-Little-Data => https://github.com/hmishra2250

• How_to_make_an_evolutionary_tetris_bot => https://github.com/IdreesInc/

• pytorch_in_5_minutes https://github.com/jcjohnson

• how_to_generate_video => https://github.com/platers

• Policy_Gradients_to_beat_Pong => https://github.com/karpathy

• hardware_demo => https://github.com/etanzapinsky

• how_to_win_slot_machines => https://github.com/awjuliani

• Game-AI => https://github.com/kihyunwon

---

Unlicensed Source Repositories

By default most works are considered "All rights reserved" when unlicensed! GitHub ToS may read as though there is room to wiggle in regards to those that Fork properly, however, it is probably best to investigate further. The OpenSource StackExchange -- How does GitHub's "forking right" cope with an "All rights reserved" project question may be worth reading too.

• Classifying_Data_Using_a_Support_Vector_Machine => https://github.com/MaviccPRP/svm/blob/master/svm-primal.ipynb

• Convolutional_neural_network => https://github.com/greydanus/pythonic_ocr

• How_to_Build_a_healthcare_startup => https://github.com/shaqian/flutter_realtime_detection

• A_Guide_to_Running_Tensorflow_Models_on_Android => https://github.com/mari-linhares/mnist-android-tensorflow

• Bitcoin_Trading_Bot => https://github.com/jaungiers/Multidimensional-LSTM-BitCoin-Time-Series

• Pokemon_GAN => https://github.com/moxiegushi/pokeGAN

• Drone_Flight_Controller => https://github.com/sksq96/deep-rl-quadcopter

• Simple_Blockchain_in_5_Minutes => https://github.com/howCodeORG/Simple-Python-Blockchain

---

Requires Manual Corrections

Some are non-GitHub sources, others are from Gists, all cannot be automatically merged via this project currently.

• How_to_make_a_chatbot => https://github.com/fchollet/keras/blob/master/examples/babi_memnn.py

• image_classifier_template =>

  • https://github.com/awesomedata/awesome-public-datasets
  • https://toolbox.google.com/datasetsearch

• recurrent_neural_network => https://gist.github.com/karpathy/d4dee566867f8291f086

• Data_Visualization => https://github.com/WillKoehrsen/Data-Analysis/blob/master/plotly/Plotly%20Whirlwind%20Introduction.ipynb

  • Note, Data_Visualization is missing the MIT license

• Time_Series_Prediction => https://github.com/anirudhpillai16/Time-Series-Analysis/blob/master/Time%20Series%20Analytics%20Vidhya.ipynb

• how_to_use_tensorboard_live => https://github.com/mamcgrath/TensorBoard-TF-Dev-Summit-Tutorial/blob/master/mnist.py

  • Note, how_to_use_tensorboard_live is missing Apache-2.0 license

• Internet_of_Things_Optimization https://github.com/dennybritz/reinforcement-learning

  • Note, Internet_of_Things_Optimization is missing the MIT license

• serverless_computing_with_google_Cloud => https://github.com/ZhengzhengYu/tensorflow/blob/master/Wide%20and%20deep%20model%20for%20income%20classification.ipynb

• Your_First_Decentralized_Application => https://gist.github.com/maheshmurthy

• math_of_machine_learning => https://github.com/Shreyas3108/house-price-prediction/blob/master/housesales.ipynb

• AI_for_video_games_demo => https://gist.github.com/malzantot

• Predicting_Winning_Teams => https://github.com/RudrakshTuwani/Football-Data-Analysis-and-Prediction/blob/master/Prediction/Scraping%20and%20Cleaning.ipynb

• neural_networks => http://iamtrask.github.io/2015/07/12/basic-python-network/

• how_to_generate_images => https://github.com/fchollet/keras/blob/master/examples/variational_autoencoder.py

---

Second Opinion Needed... Maybe

Not certain how to classify these, some or all may be okay as is, but probably best that their double checked to ensure that nothing is amiss

• Financial_Forecasting_with_TensorflowJS

  1. TensorFlow's Official CNN Article!
  2. Firdaouss Doukkali's great introductory article!
  3. Special thanks to Zaid Alyafeai for several crystal-clear code samples!
  4. https://github.com/Thoughtscript/x_team_tensorflow_js

• sirajcoin

  1. https://github.com/mappum
  2. https://github.com/chadlohrli
  3. https://github.com/keppel

• neural_networks

  • Install Conda

• Which Activation Function Should I Use => https://codereview.stackexchange.com/questions/132023/different-neural-network-activation-functions-and-gradient-descent

---

Missing ReadMe

Sources, if any will require searching for.

• azure_machine_learning

• new_repository

• IPM-Quickstart-Android

• HTTP-IPFS-Add

• dapp

• kerala_test

• go-live-video-collage

• Azadi_Complete

• Satoshi

• Azadi

• MotionSegment

• FeatureClassifier

• RetailRepunch2

• LuaTest

• Blabber

• BlabSay_Mobile_App

---

Requires re-Parsing

Seems that three repositories where missed on first-pass when searching for ReadMe files.

• Sincerely

• Pano

• PlanSegmentation

[llSourcell]

hey, thanks so much for taking the time to compile this. If i can easily fix all my repos with a few commands to properly credit the authors, I will readily do that. The problem is if i have to manually go through all of them, that's going to be so much work. Can you please create a step by step tutorial on what i should do via command line to make that happen?

[S0AndS0]

@llSourcell
hey, thanks so much for taking the time to compile this. If i can easily fix all my repos with a few commands to properly credit the authors, I will readily do that. The problem is if i have to manually go through all of them, that's going to be so much work. Can you please create a step by step tutorial on what i should do via command line to make that happen?

---

Welcome as always.

Currently this project is focused on correcting things that if left as is could lead to DMCA takedowns being issued, or worse. Reinstating original licenses and injecting commit history from original authors seems to be the simplest way of avoiding such futures.

Not every repository explicitly states that accreditation is a requirement. So language processing may be able to help with marking repositories that require that any further credits, however, with my level of Machine Learning ignorance I'd likely resort to writing in-eloquent regex search patterns. Once marked though I'd be happy to help with writing a script and/or template that can append information to ReadMe files in an automated fashion.

The main ReadMe file of this project has a Quick Start section that in the meantime may be of use, but I'll be certain to write a more detailed guide in the near future. What is the best way to notify you when it's ready for review?